Éthique et transfusion sanguine [Ethics and blood transfusion.]

Le don de sang représente un acte solidaire. Il est le plus souvent bénévole et, selon les pays et les circonstances, non rémunéré. Les éléments de la réflexion éthique sont, entre autres, l'augmentation des besoins, les critères d'exclusion de plus en plus nombreux, l'exigence sécuritaire et les modifications des structures de nos sociétés. Compte tenu de cela, les acteurs de la chaîne transfusionnelle doivent s'interroger sur certains paramètres : la valeur du don, le sens du bénévolat, l'opportunité de rémunérer l'acte de livrer une partie de soi, non plus comme un don et comme une expression d'altruisme et de solidarité, mais comme une prestation commerciale définie par des règles économiques. Blood donation is an act of solidarity. Most often, this act is done on a volunteer basis and, depending on countries and circumstances, is not remunerated. The increase in need, the always-greater number of deferral criteria, the safety issues and the changes in the structures of our societies are among the many subjects for ethical debates. Taking these into account, the actors of the transfusion must analyze certain parameters: the value of a donation, the meaning of volunteering, the appropriateness of remunerating the act of giving a part of one's self, no longer as a donation or an expression of altruism and solidarity, but as a commercial act regimented by economic laws.

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

The Original Risk: Over-Theologizing Ethics and Under-Theologizing Sin

The project of articulating a theological ethics on the basis of liturgical anthropology is bound to fail if the necessary consequence is that one has to quit the forum of critical modern rationality. The risk of Engelhardt's approach is to limit rationality to a narrow vision of reason. Sin is not to be understood as the negation of human holiness, but as the negation of divine holiness. The only way to renew theological ethics is to understand sin as the anthropological and ethical expression of the biblical message of the justification by faith only. Sin is therefore a secondary category, which can only by interpreted in light of the positive manifestation of liberation, justification, and grace. The central issue of Christian ethics is not ritual purity or morality, but experience, confession and recognition of our own injustice in our dealing with God and men.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Research Ethics Committee Activity in Northern Ireland (PDF 23 KB)

Research Ethics Committee Activity in Northern Ireland

Cinéma, machine à mondes : essai sur les films à univers multiples

Dans cet ouvrage, l'auteur propose une conceptualisation théorique de la coprésence en un même film de mondes multiples en abordant différents paramètres (hétérogénéité de la facture de l'image, pratiques du montage alterné, typologie des enchâssements, expansion sérielle, etc.) sur la base d'un corpus de films de fiction récents qui appartiennent pour la plupart au genre de la science-fiction (Matrix, Dark City, Avalon, Resident Evil, Avatar,...). Issue de la filmologie, la notion de « diégèse » y est développée à la fois dans le potentiel d'autonomisation dont témoigne la conception mondaine qui semble dominer aujourd'hui à l'ère des jeux vidéo, dans ses liens avec le récit et dans une perspective intermédiale. Les films discutés ont la particularité de mettre en scène des machines permettant aux personnages de passer d'un monde à l'autre : les modes de figuration de ces technologies sont investigués en lien avec les imaginaires du dispositif cinématographique et les potentialité du montage. La comparaison entre les films (Tron et son récent sequel, Totall Recall et son remake) et entre des oeuvres filmiques et littéraires (en particulier les nouvelles de Philip K. Dick et Simlacron 3 de Galouye) constitue un outil d'analyse permettant de saisir la contemporanéité de cette problématique, envisagée sur le plan esthétique dans le contexte de l'imagerie numérique.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

CRNINI workshop: Ethics in Research with children

Guidance for ethical research projects   •Good practice in children’s research •Building on knowledge gained in GUI •Initiated by DCYA   •Produced by Working Group with research, legal, policy and child protection expertise   Patricia's presentation is an analysis of at data from the Growing Up in Ireland study:  The relationship between family tranisitions and children's well being.

Ethics masterclass programme

The Department of Children and Youth Affairs (DCYA) recently launched ‘Guidance for developing ethical research projects involving children’. The Guidance considers ethical principles and concepts in research with children and presents a checklist for the design and conduct of research.  In this the third master class of the Children's Research Network for Ireland and Northern Ireland, participants will have an opportunity to respond to the Guidance and to discuss ethical issues in their work. The workshop will also provide participants with the opportunity to discuss the challenges facing the community and voluntary sector in applying and monitoring ethical standards in their work in the absence of formal/institutional ethics committees